Baselines for Joint-Action Reinforcement Learning of Coordination in Cooperative Multi-agent Systems

Martin Carpenter, Daniel Kudenko

Research output: Contribution to conferencePaper


A common assumption for the study of reinforcement learning of coordination is that agents can observe each other’s actions (so-called joint-action learning). We present in this paper a number of simple joint-action learning algorithms and show that they perform very well when compared against more complex approaches such as OAL [1], while still maintaining convergence guarantees. Based on the empirical results, we argue that these simple algorithms should be used as baselines for any future research on joint-action learning of coordination.
Original languageUndefined/Unknown
Publication statusPublished - 2005

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